Consumer credit databases, such as those maintained by companies who calculate credit scores, are typically enormous in size and are continually updated to reflect very recent consumer activity details for hundreds of millions of consumers. Many lenders, merchants, and other providers of credit-related products desire to exploit the wealth of data available in these databases. In particular, credit-providers would find it very advantageous to exploit the databases in order to improve or replace, at least in part, their blanket marketing campaigns, in which the credit-providers send out advertisements, make cold phone calls, or otherwise contact large numbers of consumers, based on general selection criteria, with offers of their credit-related products and/or services, in hopes that at least a portion of the contacted consumers will respond to their offers. Unfortunately, such blanket advertisement campaigns often reach consumers who are not eligible for the offers or who are not actively interested in extensions of credit and are therefore less motivated to seriously consider a lender's offer. Industry research indicates that less than 0.1% of contacts made in such blanket campaigns result in a sale.
Information in the consumer credit databases could help the credit-providers identify potentially interested and credit-worthy customers. However, several technical, database-related obstacles have kept credit providers from fully utilizing the potential of the information available from the consumer credit databases. For one, the consumer credit databases are typically organized and optimized for quick extraction of simple bits of information about individual consumers, such as individual credit scores, while allowing the database to continually insert incoming consumer data into its records.
Out of an abundance of caution, credit-providers often prefer to have complex, computationally-expensive, and time-consuming analyses and classifications performed on the database records as part of their identification of prospective customers, in an effort to avoid making the firm offer of credit that is mandated by the Fair Credit Reporting Act (FCRA) to consumers who may later in the process be revealed as being undesirable credit risks.
Furthermore, even if a solution could be found to reconcile the conflicting needs for a constantly available database of individual consumer credit-related activity versus one that allows for time-consuming and complex analytical classifications of consumers, neither model allows the credit provider to make use of newly received information from the last twenty-four hours to identify consumers who are both eligible for a firm offer of credit and are currently interested in obtaining additional credit.